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2003 Progress Report: Improved Science and Decision Support for Managing Watershed Nutrient LoadsEPA Grant Number: R830654
Title: Improved Science and Decision Support for Managing Watershed Nutrient Loads
Investigators: Chapra, Steve
Current Investigators: Chapra, Steve , Durant, John , Hemond, Harold F. , Kirshen, Paul , Vogel, Richard
Institution: Tufts University
Current Institution: Tufts University , Massachusetts Institute of Technology
EPA Project Officer: Sergeant, Anne
Project Period: January 20, 2003 through January 19, 2006 (Extended to January 19, 2007)
Project Period Covered by this Report: January 20, 2003 through January 19, 2004
Project Amount: $749,179
RFA: Nutrient Science for Improved Watershed Management (2002) RFA Text | Recipients Lists
Research Category: Water , Water and Watersheds
The objectives of this research project are to: (1) conduct scientific studies of both watershed and aquatic processes relevant to the delivery and impact of nutrients on natural waters; (2) incorporate this science into watershed and receiving water models; and (3) integrate these models into a decision-support framework that can support cost-effective environmental decisionmaking related to nutrient control of eutrophication. Although our research has national relevance, our study is being conducted on an urbanized watershed in the Boston metropolitan area, the Aberjona/Mystic River.Progress Summary:
For the past year, water samples have been collected bimonthly at eight locations along the Aberjona River and its major tributary, Horn Pond Brook, to provide baseline measures of nitrogen (N) and phosphorous (P) concentrations and speciation. Analytes include total N, ammonium, nitrate, organic N, particulate N, total P, dissolved P, and particulate P. A limited number of wet weather samples were collected to measure nutrient loads during rainstorms. These data are being analyzed to identify major sources of reduced N and P for this primarily urban and residential watershed. Furthermore, they will be used to calibrate and corroborate our watershed loading models and to drive our lake eutrophication model.
Data on nutrients and other water quality constituents also were collected in the watershed’s primary receiving water, the Upper Mystic Lake. Results support the hypothesis that P and N cycles in the lake are linked via the action of nitrate in inhibiting the benthic release of P. This hypothesis arises by analogy with nitrate-arsenic coupling in which nitrate oxidizes iron (II), which in turn scavenges and sorbs arsenate. We expected that, if arsenate and phosphate were both controlled by sorption on iron oxyhydroxides, the two chemicals would be correlated strongly throughout the water column and throughout the period of anoxia in the stratified lake. Our data have shown this to be correct.
Aside from the scientific and data collection efforts, models were developed for both the watershed and for the lake. For the watershed, the major thrust of this year's work was to modify existing models so that they: (1) could be used to simulate daily nutrient and suspended solid loadings for urban watersheds; (2) would simulate values in a form that was more directly compatible with eutrophication receiving water models; and (3) could simulate the effect of best management practices (BMPs) on loadings. With regard to the latter, simulation models of five types of BMPs were developed: street sweeping, porous pavement, grass swales, detention ponds, and bioretention units.
In addition to the foregoing research, an independent parallel effort was conducted to explore using geographic information systems technology to directly simulate placement of BMPs to optimally control watershed runoff. A distributed hydrologic model of the Aberjona Watershed was combined with a genetic algorithm (GA) to optimally locate BMPs for stormwater management. The initial application suggested that more than 20 percent of the peak flow reduction could be achieved by installing fewer than 200 BMPs in the watershed. The maximum peak flow reduction achievable was 31 percent. Although this work focused on stormwater management, it is anticipated that the approach will be applicable to nutrient load management.
A new lake model was developed to compute seasonal trends of water quality in stratified lakes. The model simulates the seasonal dynamics of nutrients (N and P), organic carbon, inorganic suspended solids, phytoplankton, and zooplankton. It is unique because it is capable of simulating the trends of the two major impacts of eutrophication: oxygen depletion and reduction of water clarity. In addition, it simulates the levels of problem phytoplankton (blue green algae), and includes a submodel of sediment-water fluxes of oxygen and nutrients.
During Year 1 of the project, a proof-of-concept decision-support system (DSS) was built by applying a GA to the lumped parameter generalized watershed loading function model to select optimal quantities of BMPs. Cost and other parameters of the various BMPs were estimated from literature values, but they are adjustable such that a user of the DSS can more accurately choose design options to suit local needs and costs that reflect local conditions. We conducted preliminary optimizations of BMP installation choices and quantities by exploring the maximum reduction of average instream nutrient concentration achievable for investment budgets of various sizes. Diminishing returns were observed, resulting from BMPs being installed first in areas generating higher loads. Additional input of BMPs then are installed successively in areas that generate smaller nutrient loads, and result in smaller water quality improvement for the same marginal BMP investment.
Finally, meetings and presentations were conducted between project team members and stakeholders in the watershed. The project goals and methodology were described during the meeting, and stakeholder reaction was elicited.Future Activities:
We will perform continued measurements on the watershed with emphasis on event sampling and areas that appear to be contributing significantly to N and P loadings in the river. In addition, we will better quantify stormwater loads of N and P from different parts of the watershed, and identify specific areas where N transformation is occurring (e.g., wetlands). In the lake, the plan for the next year is to identify the products of iron oxidation by nitrate, and specifically to determine if this is a nitrogen-conservative process (as would be the case if ammonium were the product) or a nitrogen-consuming process (if dinitrogen is formed). A more complete mass balance of the hypolimnion, including N species as well as total inorganic carbon, will serve to answer this question, which is important from the standpoint of N modeling. We also may employ 15N enrichment experiments in microcosms to test the hypothesis that either ammonium, or alternatively dinitrogen, are the products of nitrate reduction in this lake.
Further improvements to the BMP simulation model are planned through changes to the sediment routing calculations and increased sophistication of individual BMP models. We then plan to develop a semidistributed model of the Aberjona Watershed by applying the model to several subbasins, and the continuing instream data collection at various locations in the watershed will allow testing of model performance at each subbasin outlet. The lake model will be tested by applying it to our study lake as well as other lakes for which adequate data sets exist. In addition, the lake model will be integrated into the DSS. This will be conducted so that the optimization of BMPs will be based on the levels of the lake's quality variables of concern (water clarity and oxygen) that are most relevant to the stakeholders. Finally, the full stakeholder group will be convened several times to review the framework of the DSS as it evolves.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
|Other project views:||All 36 publications||5 publications in selected types||All 1 journal articles|
||Perez-Pedini C, Limbrunner JF, Vogel RM. Optimal location of infiltration-based best management practices for storm water management. Journal of Water Resources Planning and Management 2005;131(6):441-448.||
water, watersheds, land, sediments, nutrients, eutrophication, ecological effects, heavy metals, ecosystem, restoration, aquatic, decisionmaking, community based, cost benefit, environmental chemistry, biology, physics, engineering, ecology, hydrology, mathematics, limnology, modeling, northeast, U.S. Environmental Protection Agency, EPA, EPA Region I, Massachusetts, MA, Aberjona River, Mystic Lake, geographic information systems, GIS, macrophytes, decision support, stakeholders, anthropogenic processes, aquatic biota, aquatic ecosystems, nitrate concentrations, phosphorous, ammonia, oxygen, Secchi depth, clarity, suspended solids, nutrient flux, nutrient transport, restoration planning, watershed assessment, watershed management, watershed restoration., RFA, Scientific Discipline, INTERNATIONAL COOPERATION, Water, ECOSYSTEMS, Ecosystem Protection/Environmental Exposure & Risk, Aquatic Ecosystems & Estuarine Research, Water & Watershed, Aquatic Ecosystem, Water Quality Monitoring, Biochemistry, Environmental Monitoring, Terrestrial Ecosystems, Ecology and Ecosystems, Watersheds, anthropogenic processes, nutrient transport, anthropogenic stress, bioassessment, watershed classification, biodiversity, watershed management, ecosystem monitoring, nutrient flux, conservation, biota diversity, diagnostic indicators, ecosystem indicators, Mystic Lake, aquatic ecosystems, water quality, bioindicators, watershed sustainablility, biological indicators, ecosystem stress, watershed assessment, conservation planning, aquatic biota, restoration planning, watershed restoration, ecosystem response, biological impairment